from sklearn_benchmarks.reporting.hp_match import HPMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HPMatchReporting("sklearnex", config="config.yml")
reporting.run()

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.011	0.0	NaN	7.177	0.0	-1	1	NaN	NaN	0.056	0.000	0.198	0.198	See	See
5	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.011	0.0	NaN	6.996	0.0	-1	5	NaN	1.000	0.008	0.000	1.414	1.414	See	See
10	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.011	0.0	NaN	7.343	0.0	1	100	NaN	0.875	0.204	0.003	0.053	0.054	See	See
15	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.011	0.0	NaN	7.385	0.0	-1	100	NaN	NaN	0.044	0.000	0.247	0.247	See	See
20	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.011	0.0	NaN	7.380	0.0	1	5	NaN	1.000	0.001	0.000	15.596	15.802	See	See
25	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.011	0.0	NaN	7.424	0.0	1	1	NaN	0.975	0.059	0.002	0.182	0.182	See	See
30	KNeighborsClassifier_brute_force	False	fit	100000	100000	2	0.004	0.0	NaN	0.380	0.0	-1	1	NaN	NaN	0.008	0.000	0.534	0.534	See	See
35	KNeighborsClassifier_brute_force	False	fit	100000	100000	2	0.005	0.0	NaN	0.351	0.0	-1	5	NaN	1.000	0.001	0.000	6.486	6.590	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	16.870	0.018	NaN	0.000	0.017	-1	1	0.663	0.687	0.160	0.000	105.415	105.415	See	See
2	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	1.826	0.036	NaN	0.000	0.002	-1	1	0.663	1.000	0.008	0.000	224.732	224.759	See	See
3	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.313	0.006	NaN	0.000	0.313	-1	1	1.000	NaN	0.043	0.000	7.227	7.227	See	See
4	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.024	0.004	NaN	0.000	0.024	-1	1	1.000	0.742	0.163	0.006	0.144	0.144	See	See
6	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	16.947	0.022	NaN	0.000	0.017	-1	5	0.757	NaN	0.043	0.000	390.949	390.949	See	See
7	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.793	0.086	NaN	0.000	0.003	-1	5	0.757	0.875	0.199	0.002	14.047	14.048	See	See
8	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.280	0.008	NaN	0.000	0.280	-1	5	1.000	0.000	0.008	0.000	34.240	34.244	See	See
9	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.024	0.003	NaN	0.000	0.024	-1	5	1.000	NaN	0.043	0.000	0.558	0.558	See	See
11	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	15.111	0.020	NaN	0.000	0.015	1	100	0.882	0.000	0.009	0.001	1697.734	1703.140	See	See
12	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	1.855	0.003	NaN	0.000	0.002	1	100	0.882	NaN	0.043	0.000	42.671	42.671	See	See
13	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.270	0.005	NaN	0.000	0.270	1	100	1.000	0.742	0.163	0.001	1.661	1.661	See	See
14	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.019	0.000	NaN	0.000	0.019	1	100	1.000	1.000	0.008	0.000	2.333	2.333	See	See
16	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	15.088	0.114	NaN	0.000	0.015	-1	100	0.882	0.687	0.162	0.002	93.065	93.073	See	See
17	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.675	0.053	NaN	0.000	0.003	-1	100	0.882	1.000	0.008	0.000	328.250	328.302	See	See
18	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.277	0.007	NaN	0.000	0.277	-1	100	1.000	NaN	0.008	0.000	35.406	35.406	See	See
19	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.024	0.002	NaN	0.000	0.024	-1	100	1.000	0.967	0.026	0.001	0.930	0.930	See	See
21	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	15.079	0.024	NaN	0.000	0.015	1	5	0.757	NaN	0.008	0.000	1917.764	1917.764	See	See
22	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	1.846	0.004	NaN	0.000	0.002	1	5	0.757	0.974	0.027	0.001	68.700	68.770	See	See
23	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.273	0.008	NaN	0.000	0.273	1	5	1.000	1.000	0.001	0.000	389.097	393.854	See	See
24	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.019	0.000	NaN	0.000	0.019	1	5	1.000	NaN	0.008	0.000	2.402	2.402	See	See
26	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	15.346	0.006	NaN	0.000	0.015	1	1	0.663	1.000	0.001	0.000	20102.780	20457.941	See	See
27	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	1.072	0.016	NaN	0.001	0.001	1	1	0.663	NaN	0.008	0.000	134.810	134.810	See	See
28	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.281	0.005	NaN	0.000	0.281	1	1	1.000	0.975	0.060	0.002	4.675	4.677	See	See
29	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.018	0.000	NaN	0.000	0.018	1	1	1.000	1.000	0.001	0.000	24.703	25.166	See	See
31	KNeighborsClassifier_brute_force	True	predict	100000	1000	2	3.264	0.006	NaN	0.000	0.003	-1	1	0.896	0.974	0.027	0.000	120.449	120.467	See	See
32	KNeighborsClassifier_brute_force	False	predict	100000	1000	2	1.614	0.031	NaN	0.000	0.002	-1	1	0.896	1.000	0.001	0.000	2238.463	2266.828	See	See
33	KNeighborsClassifier_brute_force	True	predict	100000	1	2	0.239	0.004	NaN	0.000	0.239	-1	1	1.000	NaN	0.008	0.000	30.002	30.002	See	See
34	KNeighborsClassifier_brute_force	False	predict	100000	1	2	0.007	0.006	NaN	0.000	0.007	-1	1	1.000	0.967	0.026	0.001	0.287	0.287	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	2.829	0.0	NaN	0.028	0.0	-1	1	NaN	NaN	0.808	0.000	3.502	3.502	See	See
5	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.870	0.0	NaN	0.021	0.0	-1	5	NaN	1.000	0.000	0.000	9688.849	10470.024	See	See
10	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.902	0.0	NaN	0.021	0.0	1	100	NaN	0.940	0.570	0.003	6.845	6.845	See	See
15	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.870	0.0	NaN	0.021	0.0	-1	100	NaN	NaN	0.667	0.000	5.799	5.799	See	See
20	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.790	0.0	NaN	0.021	0.0	1	5	NaN	1.000	0.000	0.000	34609.347	42517.341	See	See
25	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.882	0.0	NaN	0.021	0.0	1	1	NaN	0.917	0.005	0.000	835.278	836.913	See	See
30	KNeighborsClassifier_kd_tree	False	fit	1000	1000	2	0.005	0.0	NaN	0.003	0.0	-1	1	NaN	NaN	0.004	0.000	1.200	1.200	See	See
35	KNeighborsClassifier_kd_tree	False	fit	1000	1000	2	0.001	0.0	NaN	0.018	0.0	-1	5	NaN	1.000	0.000	0.000	8.766	10.807	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	115.150	0.000	NaN	0.000	0.115	-1	1	0.929	0.910	0.105	0.002	1097.179	1097.332	See	See
2	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	0.432	0.002	NaN	0.000	0.000	-1	1	0.929	1.000	0.000	0.000	1570.714	1698.667	See	See
3	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.557	0.070	NaN	0.000	2.557	-1	1	1.000	NaN	0.702	0.000	3.644	3.644	See	See
4	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	-1	1	1.000	0.941	0.193	0.001	0.014	0.014	See	See
6	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	119.007	0.000	NaN	0.000	0.119	-1	5	0.946	NaN	0.713	0.000	167.022	167.022	See	See
7	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	0.857	0.008	NaN	0.000	0.001	-1	5	0.946	0.940	0.566	0.004	1.514	1.514	See	See
8	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.568	0.092	NaN	0.000	2.568	-1	5	1.000	1.000	0.001	0.000	3301.262	3458.716	See	See
9	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.003	0.000	NaN	0.000	0.003	-1	5	1.000	NaN	0.695	0.000	0.005	0.005	See	See
11	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	122.545	0.000	NaN	0.000	0.123	1	100	0.951	1.000	0.001	0.000	159237.086	167697.056	See	See
12	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	4.883	0.016	NaN	0.000	0.005	1	100	0.951	NaN	0.679	0.000	7.194	7.194	See	See
13	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.554	0.052	NaN	0.000	2.554	1	100	1.000	0.941	0.194	0.002	13.135	13.136	See	See
14	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.004	0.000	NaN	0.000	0.004	1	100	1.000	1.000	0.000	0.000	9.369	9.972	See	See
16	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	129.302	0.000	NaN	0.000	0.129	-1	100	0.951	0.910	0.106	0.000	1220.954	1220.968	See	See
17	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	2.900	0.031	NaN	0.000	0.003	-1	100	0.951	1.000	0.000	0.000	10941.527	12036.558	See	See
18	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.567	0.100	NaN	0.000	2.567	-1	100	1.000	NaN	0.001	0.000	2613.422	2613.422	See	See
19	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.005	0.001	NaN	0.000	0.005	-1	100	1.000	0.879	0.000	0.000	11.207	11.569	See	See
21	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	136.416	0.000	NaN	0.000	0.136	1	5	0.946	NaN	0.003	0.000	52692.401	52692.401	See	See
22	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	1.459	0.005	NaN	0.000	0.001	1	5	0.946	0.905	0.001	0.000	1955.772	1981.577	See	See
23	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.559	0.062	NaN	0.000	2.559	1	5	1.000	1.000	0.000	0.000	23325.247	29438.933	See	See
24	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.001	0.000	NaN	0.000	0.001	1	5	1.000	NaN	0.005	0.000	0.323	0.323	See	See
26	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	134.368	0.000	NaN	0.000	0.134	1	1	0.929	1.000	0.000	0.000	1161139.318	1390970.532	See	See
27	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	0.784	0.004	NaN	0.000	0.001	1	1	0.929	NaN	0.011	0.000	70.258	70.258	See	See
28	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.587	0.121	NaN	0.000	2.587	1	1	1.000	0.917	0.005	0.000	545.121	546.532	See	See
29	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.001	0.000	NaN	0.000	0.001	1	1	1.000	1.000	0.000	0.000	8.247	10.027	See	See
31	KNeighborsClassifier_kd_tree	True	predict	1000	1000	2	0.038	0.000	NaN	0.000	0.000	-1	1	0.891	0.905	0.001	0.000	49.264	50.087	See	See
32	KNeighborsClassifier_kd_tree	False	predict	1000	1000	2	0.021	0.001	NaN	0.001	0.000	-1	1	0.891	1.000	0.000	0.000	197.719	240.653	See	See
33	KNeighborsClassifier_kd_tree	True	predict	1000	1	2	0.005	0.000	NaN	0.000	0.005	-1	1	1.000	NaN	0.008	0.000	0.671	0.671	See	See
34	KNeighborsClassifier_kd_tree	False	predict	1000	1	2	0.002	0.000	NaN	0.000	0.002	-1	1	1.000	0.879	0.000	0.000	5.124	5.289	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	False	fit	1000000	1000000	2	0.763	0.0	30	0.021	0.0	random	NaN	30	NaN	0.394	0.0	1.937	1.937	See	See
3	KMeans_tall	False	fit	1000000	1000000	2	0.555	0.0	30	0.029	0.0	k-means++	NaN	30	NaN	0.413	0.0	1.344	1.344	See	See
6	KMeans_tall	False	fit	1000000	1000000	100	5.786	0.0	30	0.138	0.0	random	NaN	30	NaN	2.515	0.0	2.301	2.301	See	See
9	KMeans_tall	False	fit	1000000	1000000	100	5.550	0.0	30	0.144	0.0	k-means++	NaN	30	NaN	2.579	0.0	2.152	2.152	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	False	predict	1000000	1000	2	0.001	0.0	30	0.011	0.000	random	0.000	30	0.001	0.0	0.0	9.487	11.110	See	See
2	KMeans_tall	False	predict	1000000	1	2	0.001	0.0	30	0.000	0.001	random	1.000	30	1.000	0.0	0.0	11.422	13.460	See	See
4	KMeans_tall	False	predict	1000000	1000	2	0.001	0.0	30	0.011	0.000	k-means++	0.001	30	0.001	0.0	0.0	10.194	11.774	See	See
5	KMeans_tall	False	predict	1000000	1	2	0.001	0.0	30	0.000	0.001	k-means++	1.000	30	1.000	0.0	0.0	11.307	13.384	See	See
7	KMeans_tall	False	predict	1000000	1000	100	0.002	0.0	30	0.495	0.000	random	0.002	30	0.002	0.0	0.0	4.674	5.186	See	See
8	KMeans_tall	False	predict	1000000	1	100	0.001	0.0	30	0.001	0.001	random	1.000	30	1.000	0.0	0.0	10.895	13.133	See	See
10	KMeans_tall	False	predict	1000000	1000	100	0.002	0.0	30	0.455	0.000	k-means++	0.002	30	0.002	0.0	0.0	6.801	7.395	See	See
11	KMeans_tall	False	predict	1000000	1	100	0.001	0.0	30	0.001	0.001	k-means++	1.000	30	1.000	0.0	0.0	11.750	14.168	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	False	fit	10000	10000	2	0.075	0.0	20	0.002	0.0	random	NaN	20	NaN	0.638	0.0	0.118	0.118	See	See
3	KMeans_short	False	fit	10000	10000	2	0.209	0.0	20	0.001	0.0	k-means++	NaN	20	NaN	0.081	0.0	2.588	2.588	See	See
6	KMeans_short	False	fit	10000	10000	100	0.198	0.0	20	0.040	0.0	random	NaN	20	NaN	0.116	0.0	1.702	1.702	See	See
9	KMeans_short	False	fit	10000	10000	100	0.591	0.0	20	0.014	0.0	k-means++	NaN	20	NaN	0.303	0.0	1.949	1.949	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	False	predict	10000	1000	2	0.002	0.000	20	0.009	0.000	random	0.002	20	0.001	0.001	0.001	2.151	3.256	See	See
2	KMeans_short	False	predict	10000	1	2	0.001	0.000	20	0.000	0.001	random	1.000	20	1.000	0.000	0.000	10.449	12.088	See	See
4	KMeans_short	False	predict	10000	1000	2	0.002	0.000	20	0.009	0.000	k-means++	0.002	20	0.001	0.000	0.000	3.527	3.599	See	See
5	KMeans_short	False	predict	10000	1	2	0.001	0.000	20	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.000	11.228	13.318	See	See
7	KMeans_short	False	predict	10000	1000	100	0.003	0.000	20	0.319	0.000	random	0.321	20	0.311	0.001	0.000	2.534	2.557	See	See
8	KMeans_short	False	predict	10000	1	100	0.002	0.000	20	0.001	0.002	random	1.000	20	1.000	0.000	0.000	9.261	10.332	See	See
10	KMeans_short	False	predict	10000	1000	100	0.019	0.043	20	0.042	0.000	k-means++	0.381	20	0.286	0.001	0.000	19.273	19.460	See	See
11	KMeans_short	False	predict	10000	1	100	0.012	0.030	20	0.000	0.012	k-means++	1.000	20	1.000	0.000	0.000	72.839	83.579	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	False	fit	1000000	1000000	100	10.907	0.0	[20]	0.073	0.000	NaN	NaN	NaN	NaN	NaN	1.718	0.0	6.349	6.349	See	See
3	LogisticRegression	False	fit	1000	1000	10000	4.149	0.0	[26]	0.019	0.004	NaN	NaN	NaN	NaN	NaN	0.756	0.0	5.489	5.489	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	False	predict	1000000	1000	100	0.000	0.0	[20]	2.992	0.0	NaN	NaN	NaN	NaN	0.56	0.000	0.0	0.796	0.844	See	See
2	LogisticRegression	False	predict	1000000	1	100	0.000	0.0	[20]	0.013	0.0	NaN	NaN	NaN	NaN	1.00	0.000	0.0	0.375	0.424	See	See
4	LogisticRegression	False	predict	1000	100	10000	0.002	0.0	[26]	4.751	0.0	NaN	NaN	NaN	NaN	0.35	0.003	0.0	0.559	0.561	See	See
5	LogisticRegression	False	predict	1000	1	10000	0.000	0.0	[26]	0.829	0.0	NaN	NaN	NaN	NaN	0.00	0.001	0.0	0.140	0.143	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	False	fit	1000	1000	10000	0.175	0.0	NaN	0.457	0.0	NaN	NaN	NaN	0.171	0.0	1.023	1.023	See	See
3	Ridge	False	fit	1000000	1000000	100	1.425	0.0	NaN	0.562	0.0	NaN	NaN	NaN	0.825	0.0	1.726	1.726	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	False	predict	1000	1000	10000	0.01	0.0	NaN	7.887	0.0	NaN	NaN	0.083	0.017	0.0	0.600	0.600	See	See
2	Ridge	False	predict	1000	1	10000	0.00	0.0	NaN	1.131	0.0	NaN	NaN	NaN	0.000	0.0	0.687	0.844	See	See
4	Ridge	False	predict	1000000	1000	100	0.00	0.0	NaN	4.962	0.0	NaN	NaN	1.000	0.000	0.0	0.727	0.820	See	See
5	Ridge	False	predict	1000000	1	100	0.00	0.0	NaN	0.014	0.0	NaN	NaN	NaN	0.000	0.0	0.640	0.755	See	See